Depth sensors or inertial body sensors have been used for measurement or recognition of human body movements spanning various applications including healthcare rehabilitation and consumer electronics entertainment applications. Each of the above two sensors has been used individually for body movement measurements and recognition. However, each sensor has limitations when operating under real world conditions.
The application of depth sensors has been steadily growing for body movement measurements and recognition. For example, depth images captured by depth sensors have been used to recognize American Sign Language (ASL). Depth sensors typically utilize one of two major matching techniques for gesture recognition including: Dynamic Time Warping (DTW) and Elastic Matching (EM). Statistical modeling techniques, such as particle filtering and Hidden Markov model (HMM), have also been utilized for gesture recognition utilizing a depth sensor alone.
Inertial body sensors have also been utilized to recognize body movement measurements and recognition. For example, the human motion capture system may utilize wireless inertial sensors. Wireless body sensors have been utilized to recognize the activity and position of upper trunk and lower extremities. A support vector machine (SVM) classifier has been used to estimate the severity of Parkinson disease symptoms. Furthermore, Kalman filtering has been used to obtain orientations and positions of body limbs. However, the use of inertial body sensors with depth sensors at the same time and together to increase system recognition robustness has not been well developed.